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CrewAI

安装

pip install 'crewai[tools]'

CrewAI 使用 Ollama 运行本地 LLM

.env

OPENAI_API_BASE=http://localhost:11434/v1
OPENAI_MODEL_NAME=aya:8b
OPENAI_API_KEY=NULL

agent.py

版本1

每次执行结果都不一样

from dotenv import load_dotenv
load_dotenv()

from crewai import Agent, Task, Crew
from langchain_openai import ChatOpenAI


general_agent = Agent(
    role = "数学教授", 
    goal = """为提问数学问题的学生提供解决方案并给出答案。""", 
    backstory = """您是一位优秀的数学教授，喜欢以每个人都能理解的方式解决数学问题。""", 
    allow_delegation = False,
    verbose = True
)

task = Task (
    description="""3 + 5 = """,
    agent = general_agent,
    expected_output="一个数字答案。"
)

crew = Crew(
    agents=[general_agent],
    tasks=[task],
    verbose=2
)

result = crew.kickoff()

print(result)

版本2

稳定地生成结果

from dotenv import load_dotenv
load_dotenv()

from crewai import Agent, Task, Crew
from langchain_openai import ChatOpenAI


llm = ChatOpenAI(
    model = "aya:8b",
    base_url = "http://localhost:11434/v1",
    temperature=0   # 稳定地生成结果
)

general_agent = Agent(
    role = "数学教授", 
    goal = """为提问数学问题的学生提供解决方案并给出答案。""", 
    backstory = """您是一位优秀的数学教授，喜欢以每个人都能理解的方式解决数学问题。""", 
    allow_delegation = False,
    verbose = True,
    llm = llm
)

task = Task (
    description="""3 + 5 = """,
    agent = general_agent,
    expected_output="一个数字答案。"
)

crew = Crew(
    agents=[general_agent],
    tasks=[task],
    verbose=2
)

result = crew.kickoff()

print(result)

执行结果

 [DEBUG]: == Working Agent: 数学教授
 [INFO]: == Starting Task: 3 + 5 = 


> Entering new CrewAgentExecutor chain...
I now can give a great answer
Final Answer: 8

> Finished chain.
 [DEBUG]: == [数学教授] Task output: 8


8

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